Estimation of optimized window size for hybridized kNN-random forest algorithm based image demosaicing

Image Demosaicing is gaining popularity in the field of image processing as it helps in identifying the missing elements by using already known value of surrounding pixels obtained from color filter array overlaid on bayer pattern. The proposed work aims at calculating the image quality metrics (Sig...

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Bibliographic Details
Main Authors: Kaur Walia, Gurjot, Singh Sidhu, Jagroop
Format: Conference Proceeding
Language:English
Subjects:
Demosaicing
kNN
PSNR
Random Forest
SNR
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Summary:Image Demosaicing is gaining popularity in the field of image processing as it helps in identifying the missing elements by using already known value of surrounding pixels obtained from color filter array overlaid on bayer pattern. The proposed work aims at calculating the image quality metrics (Signal to Noise Ratio and Peak Signal to Noise Ratio) for finding the optimized window size by performing image demosaicing based on two machine learning algorithms i.e. hybrid kNN and random forest algorithm with 20 learning cycles. To pick out the best out of many possible window sizes (3×3, 5x3, 5x5, 7x5), a set of standard images from Kodak database have been taken into consideration. The proposed work of hybrid algorithm has been implemented in MATLAB tool. The findings of the proposed work show that 7x5 window size outperforms the other window sizes for almost all the images as well as from its counterpart kNN only.
ISSN:2214-7853
2214-7853
DOI:10.1016/j.matpr.2022.07.017